1. Field of the Invention
The present invention applies to the field of monitoring and maintaining an enterprise's information technology infrastructure and, in particular, to predicting performance irregularities in an enterprise's information technology infrastructure, and inferring possible problems causing the irregularities.
2. Description of the Related Art
Modern businesses rely on information technology (IT) to assist in carrying out business tasks. An enterprise's IT infrastructure needs to consistently perform to specification to ensure the success of the business. The IT infrastructure may be used for an enterprise's communication, database management, inventory tracking, shipment records, website management, business-to-business (B2B) ecommerce, business-to-consumer (B2C) ecommerce, accounting, billing, order tracking, customer support tracking, document management, and a possibly infinite number of other tasks. The IT infrastructure is made up of numerous components such as servers, hubs, switches, computers, the links connecting these devices, the software applications running these devices, and so on. Each of these components generally has numerous subcomponents such as a central processing unit (CPU), bus, memory, and similar high-level electrical components. The proper functioning of the IT infrastructure in large part depends on the proper functioning of these components and subcomponents.
Recognizing the importance of proper functioning, various monitoring tools have been developed to measure the performance of components and subcomponents. For example, a monitor may alter states depending on whether a computer is online or offline. If the monitor indicates the computer is offline, technicians may need to take action to put it back online, or switch to a backup computer. For relatively simple and small-scale IT infrastructures such monitoring may be adequate. However, some enterprises have IT infrastructures so large and complex that merely having information about individual components or subcomponents may not be very helpful.
For example, in a large-scale IT infrastructure that may include many servers, having a particular combination of sever failures may not cause a problem. However, if a server failure combination does cause a problem so that the enterprise's website stops functioning properly, it may not be quickly determined which server or servers of the failed servers are the critical servers causing the problem. The IT technicians receive no assistance from prior art monitors in how to prioritize technical involvement or in identifying what specific combination of components or subcomponents may be the cause of the particular problem. Furthermore, prior art monitors are incapable of predicting when problems or out-of-compliance conditions, i.e. performance irregularities may occur. Thus, IT technicians must wait until the IT infrastructure fails to function according to specification, and then they must find the cause of the failure without assistance from the monitors, other than the raw data they provide.